Search results for " Prediction"

showing 10 items of 366 documents

A method for the time-varying nonlinear prediction of complex nonstationary biomedical signals

2009

A method to perform time-varying (TV) nonlinear prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k -nearest neighbor local linear approximation to perform nonlinear prediction. The approach provides reasonable nonlinear prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of nonlinearity is not due to nonstationarity itself. The approach is t…

Time FactorsComputer scienceSpeech recognitionChaoticBiomedical EngineeringBasis functionModels BiologicalSurrogate dataYoung AdultHeart RatePredictive Value of TestsNonstationary signalHumansComputer SimulationEEGPredictabilitySignal processingNonlinear dynamicElectroencephalographySignal Processing Computer-AssistedComplexityLocal nonlinear predictionNonlinear systemNonlinear DynamicsAutoregressive modelData Interpretation StatisticalSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaLinear approximationSurrogate dataAlgorithmHeart rate variability (HRV)Algorithms
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Prediction of lncRNA-Disease Associations from Tripartite Graphs

2021

The discovery of novel lncRNA-disease associations may provide valuable input to the understanding of disease mechanisms at lncRNA level, as well as to the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of potential disease-lncRNA associations can effectively decrease time and cost of biological experiments. We propose an approach for the prediction of lncRNA-disease associations based on neighborhood analysis performed on a tripartite graph, built upon …

Tripartite graphsDecision support systemComputer scienceDisease mechanismsIdentification (biology)lncRNA-disease associations predictionDiseaseComputational biologyTime complexityGraphDecision support
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Identifying Rossby wave trains and quantifying their properties

2013

A novel method is introduced to automatically identify upper-level Rossby wave trains and to objectively diagnose their properties. Based on the envelope of the upper tropospheric meridional wind represented in a Hovmoller diagram, the algorithm identifies individual Rossby wave trains as objects. These depend to some extent on user defined parameters. The utility of the method is demonstrated in two areas of application. First, the skill of a particular numerical weather prediction model is analysed for a specific case of a long-lived Rossby wave train. For this purpose, a novel diagnostic is designed based on a Hovmoller diagram of the Rossby wave train objects that contains forecast data…

TroposphereAtmospheric ScienceSeries (mathematics)MeteorologyDiagramRossby waveTrainHovmöller diagramNumerical weather predictionEnvelope (mathematics)GeologyQuarterly Journal of the Royal Meteorological Society
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The predictive power of power-laws: An empirical time-arrow based investigation

2022

The efficient market hypothesis forbids any predictability towards future, but there is no such restriction in the case of reversed-looking approaches. We analyze if this asymmetry in non-predictability is reflected in the statistical features of financial time series. Our study is based on the analysis of the length-distribution of periods with high variability, and introduces time-asymmetric modifications of the method which are capable of revealing differences of the time series in forward and reversed time. We show that the future and reversed-looking time-series possess very similar properties, with some features being distinguishable with our method. Our findings give also evidence of…

TurbulenceStructure functionGeneral MathematicsApplied MathematicsPower-lawGeneral Physics and AstronomyForward and reversed time-seriePrice predictionStatistical and Nonlinear PhysicsIntermittency
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JSSPrediction: a Framework to Predict Protein Secondary Structures Using Integration

2006

Identifying protein secondary structures is a difficult task. Recently, a lot of software tools for protein secondary structure prediction have been produced and made available on-line, mostly with good performances. However, prediction tools work correctly for families of proteins, such that users have to know which predictor to use for a given unknown protein. We propose a framework to improve secondary structure prediction by integrating results obtained from a set of available predictors. Our contribution consists in the definition of a two phase approach: (i) select a set of predictors which have good performances with the unknown protein family, and (ii) integrate the prediction resul…

Two phase approachProtein familyComputer sciencebusiness.industryProtein secondary structure predictioncomputer.software_genreTask (project management)Set (abstract data type)Bioinformatics Protein PredictionSoftwareData miningbusinessProtein secondary structurecomputer
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Chiral Low-Energy Constants: Status and Prospects

2007

7 pages.-- PACS nrs.: 11.15.Pg, 12.38.-t, 12.39.Fe.-- ISI Article Identifier: 000252187200017.-- ArXiv pre-print available at: http://arxiv.org/abs/0710.4405

UNESCO::FÍSICA::Física molecular[PACS] Chiral LagrangiansHigh Energy Physics::LatticeHigh Energy Physics::PhenomenologyUNESCO::FÍSICAHadronicFOS: Physical sciencesPerturbation theoryChiral LagrangianQCDLEC'sHigh Energy Physics - PhenomenologyHigh Energy Physics - Phenomenology (hep-ph):FÍSICA [UNESCO]:FÍSICA::Física molecular [UNESCO]Resonance region[PACS] Quantum chromodynamics (QCD)Chiral Lagrangian; LEC's ; Prediction ; Hadronic ; QCDPrediction[PACS] Expansions for large numbers of components (e.g. 1/Nc expansions)
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A comprehensive analysis of Universal Soil Loss Equation-based models at the Sparacia experimental area

2020

Improving Universal Soil Loss Equation (USLE)‐based models has large interest because simple and reliable analytical tools are necessary in the perspective of a sustainable land management. At first, in this paper, a general definition of the event rainfall‐ runoff erosivity factor for the USLE‐based models, REFₑ = (QR)ᵇ¹(EI₃₀)ᵇ², in which QR is the event runoff coefficient, EI₃₀ is the single‐storm erosion index, and b₁ and b₂ are coefficients, was introduced. The rainfall‐runoff erosivity factors of the USLE (b₁ = 0 and b₂ = 1), USLE‐M (b₁ = b₂ = 1), USLE‐MB (b₁ ≠ 1 and b₂ = 1), USLE‐MR (b₁ = 1 and b₂ ≠ 1), USLE‐MM (b₁ = b₂ ≠ 1), and USLE‐M2 (b₁ ≠ b₂ ≠ 1) can be defined using REFₑ. Then t…

USLE-type erosion modelssoil erosion010504 meteorology & atmospheric sciencesevent soil lo0207 environmental engineeringsoil loss prediction02 engineering and technology01 natural sciencesPlot (graphics)Term (time)Data setUniversal Soil Loss EquationStatisticsExponentErosionSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-Forestali020701 environmental engineeringSurface runoff0105 earth and related environmental sciencesWater Science and TechnologyEvent (probability theory)Mathematics
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Micromechanisms of load transfer in a unidirectional carbon fibre-reinforced epoxy composite due to fibre failures: Part 3. Multiscale reconstruction…

2008

International audience; This third article describes a multiscale process which takes into account the most important microscopic phenomena associated with composite degradation, including fibre fractures and interfacial debonding, overloading of fibres neighbouring a fibre break as well as viscoelastic behaviour of the matrix. The results have been used to accurately predict the macroscopic failure of unidirectional carbon fibre-reinforced epoxy and quantify damage accumulation in pressure vessels made of the same material. The approach described has allowed the acoustic emission activity resulting from fibres breaks to be evaluated and shown how the residual lifetimes of such vessels, whe…

Unidirectional compositeMaterials scienceComposite number[ PHYS.COND.CM-MS ] Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci]02 engineering and technologyViscoelasticity0203 mechanical engineeringComposite materialCivil and Structural EngineeringFibre failuresDelaminationPressure vesselsMicromechanicsEpoxy021001 nanoscience & nanotechnologyDurabilityPressure vessel020303 mechanical engineering & transportsAcoustic emissionFailure predictionvisual_artCeramics and Compositesvisual_art.visual_art_medium[PHYS.COND.CM-MS]Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci]Multiscale processMicromechanics0210 nano-technology
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Continuous Discharge Monitoring Using Non-contact Methods for Velocity Measurements: Uncertainty Analysis

2014

At gauged site, water stage and discharge hydrographs can be related also during unsteady flow conditions, using the one-dimensional diffusive hydraulic model, DORA, and exploiting sporadic surface velocity measurements carried out with a radar sensor, during the rising limb of the flood. Indeed, starting from the measured surface velocity, the application of a simplified entropic velocity distribution model allows obtaining the benchmark discharge for the Manning’s roughness calibration. The aim of this work is twofold. First, to address the uncertainty of the approach. Second, to detect the minimum water level along the rising limb in which the occasional surface velocity measurement shou…

Unsteady flowRadar engineering detailsHydraulic engineeringHydrographSurface finishGeodesyGeologyUncertainty analysisConfidence and prediction bandsWater level
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Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

2022

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from schedu…

VDP::Teknologi: 500Control and OptimizationRenewable Energy Sustainability and the EnvironmentEnergy Engineering and Power TechnologyBuilding and ConstructionElectrical and Electronic Engineeringartificial intelligence; fault prediction; predictive maintenance; machine learning; neural networkEngineering (miscellaneous)Energy (miscellaneous)
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